This letter investigates the design of reduced-order observers for robot manipulators. Observer stability conditions are obtained based on a Lyapunov analysis. The proposed observer is enhanced with hybrid scheme that may adjust gains to cope possible unbounded velocities joints. Thanks such strategy, works accurately both robots driven by open-loop controllers and output feedback controllers. ...

Abstract Reduced-order models (ROMs) become increasingly popular in industrial design and optimization processes, since they allow to approximate expensive high fidelity computational fluid dynamics (CFD) simulations near real-time. The quality of ROM predictions highly depends on the placement samples spanned parameter space. Adaptive sampling strategies identify regions interest, which featur...

We propose a new data-driven reduced order model (ROM) framework that centers around the hierarchical structure of variational multiscale (VMS) methodology and utilizes data to increase ROM accuracy at modest computational cost. The VMS is natural fit for basis: In first step, we use projection separate scales into three categories: (i) resolved large scales, (ii) small (iii) unresolved scales....

We introduce a new method for speeding up the inference of deep neural networks. It is somewhat inspired by reduced-order modeling techniques dynamical systems. The cornerstone proposed maximum volume algorithm. demonstrate efficiency on networks pre-trained different datasets. show that in many practical cases it possible to replace convolutional layers with much smaller fully-connected relati...